RT Journal Article
JF Proceedings of the 2013 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
YR 2011
VO 00
IS
SP 257
TI Using machines to learn method-specific compilation strategies
A1 Duane Szafron,
A1 Ricardo Nabinger Sanchez,
A1 Marius Pirvu,
A1 Mark Stoodley,
A1 Jose Nelson Amaral,
K1
AB Support Vector Machines (SVMs) are used to discover method-specific compilation strategies in Testarossa, a commercial Just-in-Time (JiT) compiler employed in the IBM® J9 Java™ Virtual Machine. The learning process explores a large number of different compilation strategies to generate the data needed for training models. The trained machine-learned model is integrated with the compiler to predict a compilation plan that balances code quality and compilation effort on a per-method basis. The machine-learned plans outperform the original Testarossa for start-up performance, but not for throughput performance, for which Testarossa has been highly hand-tuned for many years.
PB IEEE Computer Society, [URL:http://www.computer.org]
SN
LA English
DO 10.1109/CGO.2011.5764693
LK http://doi.ieeecomputersociety.org/10.1109/CGO.2011.5764693